2.sparkSQL--DataFrames与RDDs的相互转换

原文和作者一起讨论:http://www.cnblogs.com/intsmaze/p/6613755.html

微信：intsmaze

import org.apache.spark.sql.{DataFrameReader, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}

object InferringSchema {
  def main(args: Array[String]) {

    //创建SparkConf()并设置App名称
    val conf = new SparkConf().setAppName("SQL-intsmaze")

    //SQLContext要依赖SparkContext
    val sc = new SparkContext(conf)
    //创建SQLContext
    val sqlContext = new SQLContext(sc)

    //从指定的地址创建RDD
    val lineRDD = sc.textFile("hdfs://192.168.19.131:9000/person.tzt").map(_.split(","))

    //创建case class
    //将RDD和case class关联
    val personRDD = lineRDD.map(x => Person(x(0).toInt, x(1), x(2).toInt))

    //导入隐式转换，如果不导入无法将RDD转换成DataFrame
    //将RDD转换成DataFrame
    import sqlContext.implicits._
    val personDF = personRDD.toDF

    //注册表
    personDF.registerTempTable("intsmaze")
    //传入SQL
    val df = sqlContext.sql("select * from intsmaze order by age desc limit 2")

    //将结果以JSON的方式存储到指定位置
    df.write.json("hdfs://192.168.19.131:9000/personresult")

    //停止Spark Context
    sc.stop()
  }
}
//case class一定要放到外面
case class Person(id: Int, name: String, age: Int)

/home/hadoop/app/spark/bin/spark-submit --class InferringSchema --master yarn --deploy-mode cluster --driver-memory 512m --executor-memory 512m --executor-cores 2 --queue default /home/hadoop/sparksql-1.0-SNAPSHOT.jar 

通过编程接口指定Schema（Programmatically Specifying the Schema）

当JavaBean不能被预先定义的时候，编程创建DataFrame分为三步：

从原来的RDD创建一个Row格式的RDD.

创建与RDD中Rows结构匹配的StructType，通过该StructType创建表示RDD的Schema.

通过SQLContext提供的createDataFrame方法创建DataFrame，方法参数为RDD的Schema.

import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.types._
import org.apache.spark.{SparkContext, SparkConf}

object SpecifyingSchema {
  def main(args: Array[String]) {
    //创建SparkConf()并设置App名称
    val conf = new SparkConf().setAppName("SQL-intsmaze")
    //SQLContext要依赖SparkContext
    val sc = new SparkContext(conf)
    //创建SQLContext
    val sqlContext = new SQLContext(sc)

    //从指定的地址创建RDD
    val personRDD = sc.textFile(args(0)).map(_.split(","))

    //通过StructType直接指定每个字段的schema
    val schema = StructType(
      List(
        StructField("id", IntegerType, true),
        StructField("name", StringType, true),
        StructField("age", IntegerType, true)
      )
    )

    //将RDD映射到rowRDD
    val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))

    //将schema信息应用到rowRDD上
    val personDataFrame = sqlContext.createDataFrame(rowRDD, schema)

    //注册表
    personDataFrame.registerTempTable("intsmaze")
    //执行SQL
    val df = sqlContext.sql("select * from intsmaze order by age desc ")
    //将结果以JSON的方式存储到指定位置
    df.write.json(args(1))
    //停止Spark Context
    sc.stop()
  }
}

/home/hadoop/app/spark/bin/spark-submit --class SpecifyingSchema \
--master yarn \
--deploy-mode cluster \
--driver-memory 512m \
--executor-memory 512m \
--executor-cores 2 \
--queue default \
/home/hadoop/sparksql-1.0-SNAPSHOT.jar \
hdfs://192.168.19.131:9000/person.txt hdfs://192.168.19.131:9000/intsmazeresult

/home/hadoop/app/spark/bin/spark-submit --class SpecifyingSchema --master yarn --deploy-mode client --driver-memory 512m --executor-memory 512m --executor-cores 2 --queue default /home/hadoop/sparksql-1.0-SNAPSHOT.jar hdfs://192.168.19.131:9000/person.txt hdfs://192.168.19.131:9000/intsmazeresult

在maven项目的pom.xml中添加Spark SQL的依赖

<dependency>
　　<groupId>org.apache.spark</groupId>
　　<artifactId>spark-sql_2.10</artifactId>
　　<version>1.6.2</version>
</dependency>

2.sparkSQL--DataFrames与RDDs的相互转换

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